KR102108205B1 - Error detecting method based on scene for smart device application and apparatus thereof - Google Patents

Abstract

Embodiments disclosed herein relates to a scene-based error detection method for a smart device application and an error detection apparatus for performing the same, and a scene for determining whether an error is included in a driving scene for an application performed on the smart device The present invention relates to an error detection method and apparatus. As a technical means for achieving a technical problem, according to an embodiment, a scene-based error detection method performed by an error detection apparatus of a smart device application, comprising: learning a normal scene of the application; Obtaining a driving scene of the application; And determining whether the obtained driving scene has an error based on the learning.

Description

Scene-based error detection method for smart device applications and an error detection device that performs the same {ERROR DETECTING METHOD BASED ON SCENE FOR SMART DEVICE APPLICATION AND APPARATUS THEREOF}

Embodiments disclosed herein relates to a scene-based error detection method for a smart device application and an error detection apparatus for performing the same, and a scene for determining whether an error is included in a driving scene for an application performed on the smart device The present invention relates to an error detection method and apparatus.

As the spread of smart devices becomes widespread, a large amount of smart device applications are being produced, and a process of determining the normal operation of the application must be performed before the launch of the smart device application.

In this regard, according to Korean Patent Application Publication No. 10-2017-0043198, which is a prior art document, an apparatus for detecting an error in an application source code and a method therefor, an apparatus for parsing an application source code and checking for an error in the source code itself, and The method is disclosed.

However, according to the prior art as described above, there is a problem that it is vulnerable to analyzing whether there is an error when applied to various smart devices as well as having many resources consumed for error analysis. Therefore, there is a need for a technique to compensate for this problem.

On the other hand, the above-mentioned background technology is the technical information acquired by the inventor for the derivation of the present invention or acquired in the derivation process of the present invention, and is not necessarily a known technology disclosed to the general public before filing the present invention. .

The embodiments disclosed herein have an object to disclose a scene-based error detection method for a smart device application and an error detection apparatus performing the same.

In addition, embodiments are intended to disclose a technique capable of analyzing errors when a smart device application is applied to various smart devices.

In addition, embodiments are intended to disclose a technique capable of analyzing errors including scene errors that do not affect performance of a smart device application that was not judged to be an error in the existing quality verification platform.

As a technical means for achieving the above-described technical problem, according to an embodiment, a scene-based error detection method performed by an error detection apparatus of a smart device application, comprising: learning a normal scene of the application; Obtaining a driving scene of the application; And determining whether the obtained driving scene has an error based on the learning.

According to another embodiment, a computer-readable recording medium in which a program for performing an error detection method is recorded is disclosed. At this time, the error detection method is performed by the error detection device, comprising: learning a normal scene of the application; Obtaining a driving scene of the application; And determining whether the obtained driving scene is in error based on the learning.

According to another embodiment, a computer program is disclosed that is performed by an error detection device and stored in a medium to perform an error detection method. At this time, the error detection method includes learning a normal scene of the application; Obtaining a driving scene of the application; And determining whether the obtained driving scene is in error based on the learning.

According to another embodiment, a storage unit for storing a normal scene of the smart device application; And a control unit for learning the normal scene and determining whether an error of the driving scene obtained by acquiring a driving scene of the smart device application is based on the learning.

According to any one of the above-described problem solving means, the embodiments disclosed herein may disclose a scene-based error detection method for a smart device application and an error detection apparatus performing the same.

In addition, embodiments may disclose a technique for analyzing errors when a smart device application is applied to various smart devices.

In addition, embodiments may disclose a technique for analyzing errors including scene errors that do not affect performance of a smart device application that was not judged to be an error in the existing quality verification platform.

The effects obtained in the disclosed embodiments are not limited to the above-mentioned effects, and other effects not mentioned are obvious to those skilled in the art to which the embodiments disclosed from the following description belong. Can be understood.

1 is a block diagram showing the configuration of an error detection apparatus according to an embodiment.
2 to 4 are flowcharts for describing an error detection method according to one embodiment.

Hereinafter, various embodiments will be described in detail with reference to the accompanying drawings. The embodiments described below may be embodied in various different forms. In order to more clearly describe the features of the embodiments, detailed descriptions of the items well known to those of ordinary skill in the art to which the following embodiments pertain are omitted. In the drawings, parts irrelevant to the description of the embodiments are omitted, and like reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a component is "connected" to another component, this includes not only "directly connected" but also "connected with other components in between". In addition, when a configuration is said to "include" a configuration, this means that, unless specifically stated otherwise, it may mean that other configurations may be included as well.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings.

1 is a block diagram showing the configuration of an error detection apparatus 10 according to an embodiment.

The error detection apparatus 10 according to an embodiment disclosed in the present specification may detect an error of a smart device application based on a scene. According to an embodiment, the error detecting apparatus 10 may detect an error in a driving scene of the smart device application by learning a normal scene of the smart device application according to machine learning, for example, deep learning.

In this case, the normal scene refers to a scene without errors among driving scenes output when the smart device application is executed on the smart device. For example, if there is no breakage in the driving scene, the input button is output at the set position, and if there is no horizontal or vertical clipping of the scene, it can be determined as a normal scene.

According to an embodiment, the error detection device 10 may be implemented as an electronic terminal in which an application for performing an error detection method is installed, or may be implemented as a server or a server-client system. When the error detection device 10 is implemented as a server-client system, it may include a user terminal in which a client for interaction with a user is installed, wherein the user terminal may be implemented as an electronic terminal.

In this case, the electronic terminal may be implemented as a computer, a portable terminal, a television, a wearable device, or the like, which may include an interface capable of interacting with a user. Here, the computer includes, for example, a laptop equipped with a web browser (WEB Browser), a desktop (desktop), a laptop (laptop), and the like, and the portable terminal is, for example, a wireless communication device that guarantees portability and mobility. , PCS (Personal Communication System), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), GSM (Global System for Mobile communications), IMT (International Mobile Telecommunication) -2000, CDMA (Code All kinds of handhelds such as Division Multiple Access (2000), W-Code Division Multiple Access (W-CDMA), Wireless Broadband Internet (Wibro), Smart Phone, Mobile Worldwide Interoperability for Microwave Access (WiMAX), etc. (Handheld) -based wireless communication device. In addition, the television may include Internet Protocol Television (IPTV), Internet Television (TV), terrestrial TV, and cable TV. Furthermore, the wearable device is a type of information processing device that can be directly worn on the human body, for example, a watch, glasses, accessories, clothing, shoes, etc., to connect to a remote server or other terminal through a network directly or through another information processing device. And can be connected.

In addition, the server may be implemented as a computing device capable of communicating through a network with an electronic terminal on which a client is installed for interaction with a user, and may include a storage device capable of storing data, or transmit data through a third server. You can also save.

A specific configuration will be described with reference to FIG. 1. Referring to FIG. 1, the error detection device 10 according to an embodiment may include an input / output unit 11, a storage unit 12, a communication unit 13, and a control unit 14.

The input / output unit 11 according to an embodiment may include an input unit for receiving input from a user, and an output unit for displaying information such as a result of performing a job or a state of the error detection device 10. For example, the input / output unit 11 may include an operation panel for receiving user input, a display panel for displaying the screen, and the like.

Specifically, the input unit may include devices capable of receiving various types of user input, such as a photographing device, a keyboard, a physical button, a touch screen, a camera, or a microphone. In addition, the output unit may include a display panel, a speaker or a headset. However, the present invention is not limited thereto, and the input / output unit 11 may include a configuration supporting various input / output.

Meanwhile, the storage unit 12 may store data for performing an error detection method according to an embodiment. For example, input data for performing the error detection method may be stored, and output data may be stored as a result of performing the error detection method. For example, a database storing the normal scene may be included in order to learn the normal scene of the smart device application.

Meanwhile, the communication unit 13 may perform wired / wireless communication with other electronic terminals or networks. To this end, the communication unit 13 may include a communication module supporting at least one of various wired and wireless communication methods. For example, the communication module may be implemented in the form of a chipset.

At this time, the wireless communication supported by the communication unit 13 may be, for example, Wi-Fi (Wireless Fidelity), Wi-Fi Direct, Bluetooth (Bluetooth), UWB (Ultra Wide Band), or NFC (Near Field Communication). In addition, the wired communication supported by the communication unit 13 may be, for example, USB or High Definition Multimedia Interface (HDMI).

Meanwhile, the control unit 14 controls the overall operation of the error detection device 10 and may include a processor such as a CPU. At this time, the control unit 14 may include an accelerator, for example, a GPU, to smoothly perform an error detection method such as deep learning.

According to an embodiment, the controller 14 may learn a normal scene of the application in order to detect an error of the smart device application (hereinafter, an application). According to an embodiment, the normal scene may be stored in the database of the storage unit 12.

At this time, the control unit 14 may learn a normal scene by deep learning based on one-class classification. For example, the controller 14 may deeply learn a normal scene based on an autoencoder. Through this, the control unit 14 can build a normal judgment model for determining an error in the scene.

In addition, the control unit 14 may acquire a driving scene of the application in order to detect an error of the application.

To this end, the controller 14 may drive the application according to the setting and obtain a driving scene generated according to driving. At this time, the control unit 14 may sequentially execute the application and sequentially acquire a driving scene that is output. Also, according to an embodiment, one or more applications may be driven to obtain a driving scene for each of the one or more applications.

Here, the setting may include a setting of a smart device to which the application is applied. According to an embodiment, when settings of one or more smart devices are different, an application may be driven according to each setting of the smart devices, and the driving scene of the obtained application may be matched and stored with identification information of each smart device.

At this time, the setting may include a setting according to the resolution of the smart device. In this case, the controller 14 may drive the application according to one or more resolutions.

In addition, the controller 14 may determine whether the obtained driving scene has an error, but may determine whether the driving scene has an error based on the learning of the normal scene. For example, the control unit 14 may determine whether the driving scene has an error based on the normal determination model constructed by deep learning.

That is, the control unit 14 may determine whether the driving scene has an error based on deep learning of the normal scene. At this time, the controller 14 may store the driving scene classified as the normal scene according to the judgment in the database of the storage unit 12 to increase the retention amount of the normal scene for learning.

In addition, the control unit 14 may classify a driving scene that is not determined to be a normal scene and store it separately from the normal scene. According to an embodiment, a driving scene not determined as a normal scene may be provided to a user, for example, an administrator who wants to detect an error in an application. For example, a report message for a driving scene that is not determined to be a normal scene may be generated or a generated report message may be transmitted to the manager terminal.

According to an embodiment, the control unit 14 may provide a driving scene that is not determined to be a normal scene to the administrator, select a normal scene from among the provided driving scenes, and store it in a database for learning a normal scene. By repeating this process, it is possible to improve the reliability of the normal judgment model.

In addition, the control unit 14 may classify a driving scene not determined as a normal scene according to the degree of error. For example, a driving scene that is not determined to be a normal scene may be classified into one or more categories according to a probability that the driving scene includes an error, that is, an error probability, and the categorized category may be selectively provided to the manager. For example, a driving scene may be classified by dividing an error probability into a predetermined section, and a driving scene included in an error probability of a predetermined section may be provided according to the administrator's selection. Alternatively, the amount of work of the manager can be reduced by providing the manager with a driving scene having a predetermined probability.

According to an embodiment, in determining whether the driving scene has an error, the control unit 14 may generate a schedule for allocating tasks to a plurality of GPUs, and determine whether the driving scene has an error according to the generated schedule.

For example, the controller 14 may schedule a task for each of the acquired driving scenes to be allocated to one GPU. That is, it can be scheduled to process a task for one driving scene on one GPU.

At this time, the control unit 14 may allocate tasks according to the order of acquiring the driving scene. For example, the task for each driving scene is assigned to the GPU in the obtained order to determine whether or not there is an error, but when a GPU has been completed, the order of acquisition of the driving scene among the tasks waiting for the GPU with the completed task is determined. You can schedule tasks to be allocated in quick order. In other words, if a task is performed on the driving scene (i-1) of the application and the GPU is completed while performing the task, the related scene is assigned by performing the task for the driving scene (i) on the completed GPU. You can make the work on. This can increase the efficiency of the work.

Also, the control unit 14 may sequentially determine whether or not an error has occurred for one or more applications. For example, when the determination of the driving scene of one application is completed, a job on the driving scene of another application may be allocated to the GPU on which the operation is completed. That is, tasks for all of the driving scenes (i-2, i-1, i, i + 1, i + 2) corresponding to one application are allocated according to the acquisition order, so that the GPU is completed during the operation. Upon detection, an error determination can be performed by sequentially assigning tasks for the driving scenes (j-2, j-1, j, j + 1, j + 2) corresponding to the next application according to the order of acquisition of the driving scenes have.

Meanwhile, FIGS. 2 to 4 are flowcharts for describing an error detection method performed by the error detection device 10. The error detection method according to the embodiment illustrated in FIGS. 2 to 4 includes steps that are processed in time series in the error detection device 10 according to the embodiment related to FIG. 1. Therefore, even if it is omitted below, the contents described above with respect to the error detection apparatus 10 according to the embodiment of FIG. 1 may also be applied to the error detection method according to the embodiments illustrated in FIGS. 2 to 4. have.

Referring to FIG. 2, the error detection device 10 may learn a normal scene of the application (S21). At this time, the error detection device 10 may deeply learn a normal scene based on a single class classification technique.

In addition, the error detection device 10 may acquire a driving scene of the application (S22). In this case, referring to FIG. 3, the error detection device 10 may acquire an operation scene generated by driving the application according to a setting (S31) and an application when acquiring a driving scene (S32).

According to an embodiment, the setting may include the setting of the smart device to which the application is applied. For example, the setting may include a resolution, and in this case, the error detection device 10 may drive an application according to one or more resolutions, and obtain a driving scene generated according to driving.

In addition, the error detection device 10 may determine whether the obtained driving scene is in error based on learning performed in S21 (S23).

According to an embodiment, the error detection device 10 may generate a schedule for assigning each of the obtained driving scenes to one GPU, and determine an error of the driving scenes according to the generated schedule. That is, the error detection device 10 may generate a schedule for allocating tasks for one driving scene to one GPU.

In addition, the error detection device 10 may generate a schedule, but may generate a schedule for allocating tasks according to the order of acquisition of the driving scene. For example, in the order in which the driving scenes are acquired, tasks for the driving scenes are assigned to each GPU, but when a GPU is completed, tasks for the driving scenes waiting for the GPUs in which the tasks are completed may be assigned in the order of acquisition. .

In addition, when the determination of one application is completed, the error detection device 10 may allocate a job for a driving scene of another application to the GPU on which the job is completed.

Meanwhile, referring to FIG. 4, the error detection device 10 may determine whether the driving scene is an error, and store the determined driving scene as a long-term scene in a database for learning a normal scene (S41).

The term '~ unit' used in the above embodiments means software or hardware components such as a field programmable gate array (FPGA) or an ASIC, and '~ unit' performs certain roles. However, '~ wealth' is not limited to software or hardware. The '~ unit' may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors. Thus, as an example, '~ unit' refers to components such as software components, object-oriented software components, class components and task components, processes, functions, attributes, and procedures. , Subroutines, segments of program patent code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays, and variables.

The functions provided within the components and '~ units' may be combined into a smaller number of components and '~ units', or separated from additional components and '~ units'.

In addition, the components and '~ unit' may be implemented to play one or more CPUs in the device or secure multimedia card.

The error detection method according to the embodiments described with reference to FIGS. 2 to 4 may also be implemented in the form of a computer-readable medium storing instructions and data executable by a computer. At this time, instructions and data may be stored in the form of program code, and when executed by a processor, a predetermined program module may be generated to perform a predetermined operation. In addition, the computer-readable medium can be any available medium that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable medium may be a computer recording medium, which is volatile and non-volatile implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Volatile, removable and non-removable media. For example, the computer recording medium may be a magnetic storage medium such as HDD and SSD, an optical recording medium such as CD, DVD and Blu-ray disk, or a memory included in a server accessible through a network.

Also, the error detection method according to the embodiments described with reference to FIGS. 2 to 4 may be implemented as a computer program (or computer program product) including instructions executable by a computer. The computer program includes programmable machine instructions processed by a processor and may be implemented in a high-level programming language, object-oriented programming language, assembly language, or machine language. . In addition, the computer program may be recorded on a tangible computer-readable recording medium (eg, memory, hard disk, magnetic / optical medium, or solid-state drive (SSD), etc.).

Therefore, the error detection method according to the embodiments described with reference to FIGS. 2 to 4 may be implemented by executing the computer program as described above by the computing device. The computing device may include at least some of a processor, a memory, a storage device, a high-speed interface connected to the memory and a high-speed expansion port, and a low-speed interface connected to the low-speed bus and the storage device. Each of these components is connected to each other using various buses, and can be mounted on a common motherboard or mounted in other suitable ways.

Here, the processor is capable of processing instructions within the computing device, such as to display graphical information for providing a graphical user interface (GUI) on an external input or output device, such as a display connected to a high-speed interface. Examples are commands stored in memory or storage devices. In other embodiments, multiple processors and / or multiple buses may be used in conjunction with multiple memories and memory types as appropriate. Also, the processor may be implemented as a chipset formed by chips including a plurality of independent analog and / or digital processors.

Memory also stores information within computing devices. In one example, the memory may consist of volatile memory units or a collection thereof. As another example, the memory may consist of non-volatile memory units or a collection thereof. The memory may also be other forms of computer-readable media, such as magnetic or optical disks.

And the storage device can provide a large storage space for the computing device. The storage device may be a computer-readable medium or a configuration including such a medium, and may include, for example, devices within a storage area network (SAN) or other configurations, and may include floppy disk devices, hard disk devices, optical disk devices, Or a tape device, flash memory, or other similar semiconductor memory device or device array.

The above-described embodiments are for illustration only, and those having ordinary knowledge in the technical field to which the above-described embodiments belong can easily be modified into other specific forms without changing the technical idea or essential features of the above-described embodiments. You will understand. Therefore, it should be understood that the above-described embodiments are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope to be protected through the present specification is indicated by the claims, which will be described later, rather than the detailed description, and should be interpreted to include all modified or modified forms derived from the meaning and scope of the claims and equivalent concepts thereof. .

10: error detection device 11: input and output unit
12: storage unit 13: communication unit
14: control

Claims (15)

In the scene-based error detection method performed by the error detection device of the smart device application,
Building a normal judgment model by learning the normal scene of the application;
Obtaining a driving scene of the application by driving the application according to each setting of a smart device to which the application is applied; And
And determining whether the obtained driving scene is in error based on the normal determination model. According to claim 1,
The step of building a normal judgment model by learning the above is
And deep learning the normal scene according to a single class classification technique. delete According to claim 1,
The obtaining step,
The method for detecting an error, characterized in that the application is driven according to one or more resolutions. According to claim 1,
The determining step,
A schedule for assigning each of the obtained driving scenes to one GPU is generated, and whether the driving scene is an error is determined according to the generated schedule. Characterized in that, assigning a task for any one of the error detection method. According to claim 1,
The determining step,
When the determination of the driving scene is completed, further comprising determining whether the driving scene of the other application is in error, the error detection method. According to claim 1,
And storing the driving scene determined as the normal scene in the database for learning. A computer-readable recording medium on which a program for performing the method according to claim 1 is recorded. A computer program carried out by an error detection device and stored in a medium to perform the method according to claim 1. A storage unit that stores a normal scene of the smart device application; And
Building a normal judgment model by learning the normal scene, and determining whether an error of the driving scene obtained by acquiring the driving scene of the application is based on the normal judgment model according to each smart device to which the application is applied. An error detection device comprising a control unit. The method of claim 10,
The control unit,
Learning the normal scene, characterized in that the deep learning according to a single class classification technique, the error detection device. delete The method of claim 10,
The control unit,
The apparatus for detecting errors, characterized in that the application is driven according to one or more resolutions. The method of claim 10,
The control unit,
A schedule for assigning each of the obtained driving scenes to one GPU is generated, and whether the driving scene is an error is determined according to the generated schedule. Characterized in that it assigns a task to any one of, the error detection device. The method of claim 10,
The control unit,
When the determination of the driving scene is completed, an error detection device characterized in that it further determines whether or not the driving scene of the other application is in error.

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